Recurrent coupling improves discrimination of temporal spike patterns

Abstract

Despite the ubiquitous presence of recurrent synaptic connections in sensory neuronal systems,
their general functional purpose is notwell understood. A recent conceptual advance
has been achieved by theories of reservoir computing in which recurrent networks have
been proposed to generate short-term memory as well as to improve neuronal representation
of the sensory input for subsequent computations. Here, we present a numerical
study on the distinct effects of inhibitory and excitatory recurrence in a canonical linear
classification task. It is found that both types of coupling improve the ability to discriminate
temporal spike patterns as compared to a purely feed-forward system, although in different
ways. For a large class of inhibitory networks, the network’s performance is optimal
as long as a fraction of roughly 50% of neurons per stimulus is active in the resulting population
code. Thereby the contribution of inactive neurons to the neural code is found to
be even more informative than that of the active neurons, generating an inherent robustness
of classification performance against temporal jitter of the input spikes. Excitatory
couplings are found to not only produce a short-term memory buffer but also to improve
linear separability of the population patterns by evoking more irregular firing as compared
to the purely inhibitory case. As the excitatory connectivity becomes more sparse, firing
becomes more variable, and pattern separability improves. We argue that the proposed
paradigm is particularly well-suited as a conceptual framework for processing of sensory
information in the auditory pathway.